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Yayın Psychological distress of breast cancer survivors during the Covid-19 pandemic and related factors: a controlled study(KARE Publication, 2023-07) Taş, Beyza; Anuk, Dilek; Akçinar Yayla, BernaOBJECTIVE: Although the prevalence of breast cancer is high among women, survival rates are increasing. How-ever, breast cancer survivors (BCS) continue to experience various psychological problems after their treatments and are also exposed to additional stressors, such as the current Coronavirus disease 2019 (COVID-19) pandemic. The aim of this study was to examine the psychological distress and related factors (social support, intolerance of uncertainty, coping strategies) of BCS during the COVID-19 pandemic and the role of breast cancer diagnosis in this process. METHODS: This study included 95 BCS and 87 healthy women. Sociodemographic Information Form and depression anxiety stress scale, social support scale, intolerance of uncertainty scale, and coping strategies short form scales were administered to the participants. T tests and regression analyses were performed to examine the relationships between the variables. RESULTS: There was no significant difference between the two groups in terms of depression and anxiety, but the stress of BCS was lower than that of healthy women. In the regression analysis, the diagnosis of breast cancer was not a predictor for depression and anxiety, but it was a significant predictor for stress. Com-mon predictors of increased depression, anxiety, and stress were decreased social support, increased uncertainty intolerance, and increased emotion-focused coping. CONCLUSION: Focusing on the development of intolerance of uncertainty, social support, and problem-focused coping strategies of psychological interventions for women BCS during epidemics such as COVID-19 may reduce their psychological distress while maintaining and increasing their psychological well-being.Yayın Son ergenlik döneminde belirsizliğe tahammülsüzlük ve aleksitimi arasındaki ilişkide anksiyetenin aracı etkisinin incelenmesi(Işık Üniversitesi, 2022-02-04) Özmen, Fatma Hilal; Aktan, Zekeriya Deniz; Işık Üniversitesi, Lisansüstü Eğitim Enstitüsü, Klinik Psikoloji Yüksek Lisans ProgramıBu araştırmanın temel amacı, son ergenlik dönemindeki bireylerde belirsizliğe tahammülsüzlük ve aleksitimi arasındaki ilişkide anksiyetenin aracı rolünü incelemektir. Araştırma kapsamında içleme ve dışlama kriterlerine uygun bulunan 430 katılımcının verisi ile analizler gerçekleştirilmiştir. Veri toplama aracı olarak kişilerin sosyodemografik bilgilerine ulaşmak için Kişisel Bilgi Formu, Belirsizliğe Tahammülsüzlük Ölçeği (BTÖ-12), Toronto Aleksitimi Ölçeği (TAÖ - 20) ve Beck Anksiyete Envanteri kullanılmıştır. Araştırmamızdaki temel hipotezleri test etmek için, basit regresyon ve doğrusal hiyerarşik regresyon analizleri kullanılmıştır. Araştırma sonucuna göre belirsizliğe tahammülsüzlük ile aleksitimi arasındaki ilişkide anksiyetenin anlamlı kısmi aracı etkisi (p=0,000) olduğu tespit edilmiştir p<0.05. Araştırma, belirsizliğe tahammülsüzlük ve aleksitimi arasındaki ilişkinin anksiyete aracılığı ile gerçekleştiğini ortaya koymuştur.Yayın Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty(Springer, 2022-04-02) Tuna, Ömer Faruk; Çatak, Ferhat Özgür; Eskil, Mustafa TanerAlthough state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These vulnerabilities make it challenging to deploy deep neural network models in the areas where security is a critical concern. In recent years, many research studies have been conducted to develop new attack methods and come up with new defense techniques that enable more robust and reliable models. In this study, we use the quantified epistemic uncertainty obtained from the model's final probability outputs, along with the model's own loss function, to generate more effective adversarial samples. And we propose a novel defense approach against attacks like Deepfool which result in adversarial samples located near the model's decision boundary. We have verified the effectiveness of our attack method on MNIST (Digit), MNIST (Fashion) and CIFAR-10 datasets. In our experiments, we showed that our proposed uncertainty-based reversal method achieved a worst case success rate of around 95% without compromising clean accuracy.
